Wan, J.X. and Ma, Y., 2020. Multi-scale spectral-spatial remote sensing classification of coral reef habitats using CNN-SVM. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 11-20. Coconut Creek (Florida), ISSN 0749-0208.
In recent years, coral reefs have undergone serious degradation globally, prompting the use of remote sensing as an effective means to monitor these reefs on a large scale. Deep learning, which is a state-of-the-art image processing technique suitable for remote-sensing applications, can be used to learn nonlinear characteristics of images and is therefore applicable to the classification of small-scale coral reefs. This paper proposes a multi-scale method based on a convolutional neural network and support vector machine (CNN-SVM) to classify the coral reef habitats of Zhaoshu Island and Zhong Island in the Xisha Archipelago, China. This method combines spectrum, texture, and bidimensional empirical mode decomposition (BEMD) based scale separation algorithm to fully learn multi-scale information of coral reefs. Remote-sensing images captured by the WorldView-2 and Gaofen-2 (GF-2) satellites are used to evaluate the performance of the proposed CNN-SVM framework. The results indicate that the proposed method performs accurately and efficiently. Compared with SVM, random forest (RF), CNN, and CNN-RF, the overall accuracy is improved by 10.57 %, and the accuracy of classifying reef-clumping areas is improved by 17.44 %.